Novel alarm correlation analysis system based on association rules mining in telecommunication networks |
| |
Authors: | Tongyan Li Xingming Li |
| |
Affiliation: | a Key Laboratory of Broadband Optical Fiber Transmission and Communication Networks of Ministry of Education, UESTC, Chengdu 610054, China b Department of Communication Engineering,Chengdu University of Information Technology, Chengdu 610225,China |
| |
Abstract: | Alarm correlation analysis system is an useful method and tool for analyzing alarms and finding the root cause of faults in telecommunication networks. Recently, the application of association rules mining becomes an important research area in alarm correlation analysis.In this paper, we propose a novel Association Rules Mining based Alarm Correlation Analysis System (ARM-ACAS) to find interesting association rules between alarm events. In order to mine some infrequent but important items, ARM-ACAS first uses neural network to classify the alarms with different levels. In addition, ARM-ACAS also exploits an optimization technique with the weighted frequent pattern tree structure to improve the mining efficiency. The system is both efficient and practical in discovering significant relationships of alarms as illustrated by experiments performed on simulated and real-world datasets. |
| |
Keywords: | Alarm correlation analysis Association rules mining Neural network Weighted frequent pattern tree Weighted potential frequent itemsets |
本文献已被 ScienceDirect 等数据库收录! |
|